Influence of Instagram Influencers in Promoting Brand Patronage in Nigeria: A Study of Pepsi Brand

Influencer marketing is an important marketing concept in today's world, and brands are utilising influencers to sell their products due to their large followership on various social media platforms such as Instagram. Hence, the study examined the influence of Pepsi brand Instagram influencers in promoting brand patronage in Nigeria. The study was anchored on technological determinism theory and attitude change theory. The study adopted a descriptive survey research design. The survey method was used to gather data from the population of 5,500,000 Instagram followers. A sample size of 385 was arrived at using the Australian sample size calculator. The systematic sampling technique was used. Questionnaire was employed as the instrument for data collection. Findings revealed that, at an average mean of 3.3 (N=380), Instagram users' level of exposure to the Pepsi brand on Instagram large extent is high. The results also revealed that the perception of Instagram users towards the Pepsi brand promoted by Instagram influencers is positive at an average mean of 3.2 (N=380). It was also further revealed that the influence of Instagram influencers makes Instagram users purchase Pepsi products at an average mean of 3.4. The researchers concluded that Instagram users have very high exposure to Pepsi brands promoted by Instagram influencers, and Instagram influencers are very effective in promoting brand patronage. It was recommended, amongst others, that Pepsi manufacturers should continue to engage the services of Instagram influencers to create more awareness for the brand among social media users

AI recognition of patient race in medical imaging: a modelling study

Background Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91-0·99], CT chest imaging [0·87-0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study. Interpretation The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging. Funding National Institute of Biomedical Imaging and Bioengineering, MIDRC grant of National Institutes of Health, US National Science Foundation, National Library of Medicine of the National Institutes of Health, and Taiwan Ministry of Science and Technology

Reading Race: AI Recognises Patient's Racial Identity In Medical Images

Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images. Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race. Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study. Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race -- even from corrupted, cropped, and noised medical images -- in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model has access to

The role of international law in resource development through foreign investment and the protection of the rights of indigenous peoples : a case study of Nigeria

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Carbon auditing in tree-soil nexus: a sustainable approach towards CO2 sequestration and environmental transformation

Anomalies in climatic behavior is threatening various aspects of life including environmental degradation, food insecurity, and widespread of diseases. The continual build-up of atmospheric carbon dioxide (CO2) and other malpractices are among the factors responsible for ecosystem degradation. Field experimentation was conducted, where stratified random sampling was employed to delineate point were Phoenix dactylifera and Mangifera indica was sampled. Experimental point was replicated twice where moist soil was examined for its organic matter and organic carbon content, before and after the experiment. The textural class of the area using USDA textural model after laboratory analysis indicated soils of University of Abuja, Federal Capital Territory of Nigeria ranging from loam to sandy-loam soils. Laboratory fractionalization indicated that the soils of the area has coarse sand value (1.8 g kg-1), fine sand content ranging from (4.5 – 5.2 g kg-1), silt content at (4.5 – 5.2g kg-1) and clay content at (72 g kg-1). Estimation analysis revealed that the organic matter and organic carbon content of the area is low to moderately low. Results of the study revealed that Phoenix dactylifera and Mangifera indica was able to sequester carbon in the form of CO2 which was audited in the form of soil organic carbon (SOC). The study thereby encourages the cultivation of Phoenix dactylifera and Mangifera indica which is not onlyeconomic trees that produce food or fuelwood, but as a climate change tool that could be used to regulate climate change in the form of CO2 sequestration

Model for Predictive Analysis of Hardness of the Heat Affected Zone in Aluminum Weldment Cooled in Groundnut Oil Relative to HAZ Hardness of Mild Steel and Cast Iron Weldments Cooled in Same Media

Abstract: Model for predictive analysis of hardness of the heat affected zone in aluminum weldment cooled in groundnut oil has been derived. The general model; β = 0.5997√(γα) is dependent on the hardness of the heat affected zone (HAZ) in mild steel and cast iron weldments cooled in same media. Furthermore, re-arrangement of these models could be done to evaluate the HAZ hardness of mild steel or cast iron respectively as in the case of aluminum

Mapping soil organic carbon-soil biodiversity variability in the ecosystem-nexus of tropical soils

It is no more news that the deterioration of our mother Earth has resulted in many hardships faced in many lands of the world. Research statistics has shown that about 80% of the environmental problems faced in Asia, especially the loss of soil biodiversity results from deforestation. Africa has been intensely affected by the hazards of climate change at a rate of more than 50%, also Near East and North Africa has recorded more than 48% loss of her biodiversity in soils due to habitat alteration and loss. This list is inexhaustive and heart-broken, presenting a view that if sustainable remediation is not taken then we will have more malnourished and sick people in years to come, our environment will be more polluted and toxic, our water system will become more and more difficult to remediate, there could be increase in local, national and international conflict among other unforeseen unpleasant happenings. To contribute as a modality towards solving this problem this study investigated the current soil organic carbon-soil biodiversity variability in the ecosystem-nexus of soils. The study took place within the University of Abuja landmass. Spatial and temporal data were collected on earth-system properties, were analysis and simulations were done. The Area was model and interpolated to find hot spots with grave threat. Explorative and descriptive statistics was applied in the study. Results indicated that the soils of the study area are compacted and hence unfit to support sustainable survival of the living entities within the soil system, with soil Bulk density value range at 2.1g cm-3–2.71g cm-3. Organic carbon of the area was low. Geotechnical and geomorphological evaluation and interactions revealed only two (2) points having earthworm length of 1 cm which presented a view that the soils spore is too tight to enable sustainable flourishing of below and above ground biodiversity in the sites investigated. Hence ecological tool like the use of Vetiver Grass Technology was recommended for the study area environmental regeneration and for healing the soils impediment

<i>Cucumeropsis mannii</i> seed oil protects against bisphenol A-induced hepatotoxicity by mitigating inflammation and oxidative stress in rats

From Crossref journal articles via Jisc Publications RouterHistory: epub 2023-10-20, issued 2023-10-20Article version: AMPublication status: PublishedOBJECTIVES This study looked at how CMSO affected male Wistar albino rats' liver damage caused by bisphenol A. METHODS The standard HPLC method was used to assess the CMSO's phenolic content. Then, six (n = 8) groups of forty-eight (48) male Wistar rats (150 20 g) each received either CMSO or olive oil before being exposed to BPA for 42 days. Groups: A (one milliliter of olive oil, regardless of weight), B (BPA 100 mg/kg body weight (BW)), C (CMSO 7.5 mg/kg BW), D (CMSO 7.5 mg/kg BW + BPA 100 mg/kg BW), E (CMSO 5.0 mg/kg BW + BPA 100 mg/kg BW), and F (CMSO 2.5 mg/kg BW + BPA 100 mg/kg BW). KEY FINDINGS A surprising abundance of flavonoids, totaling 17.8006 10.95 g/100 g, were found in the HPLC data. Malondialdehyde, liver enzymes, reactive oxygen species, total bilirubin, and direct bilirubin levels were all significantly elevated by BPA (p 0.05). Additionally, nuclear factor-B, interleukin-6, interleukin-1, tumor necrosis factor, and histological alterations were all considerably (p 0.05) caused by BPA. The altered biochemical markers and histology were, however, noticeably recovered by CMSO to a level that was comparable to the control. CONCLUSION Due to the abundance of flavonoid components in the oil, CMSO protects the liver from BPA-induced hepatotoxicity by lowering oxidative stress and inflammatory reactions